基于SBDART和BP的夜间雾遥感检测和能见度反演
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摘要
为了挖掘卫星影像中的雾信息为雾预测预报服务,利用双红外亮温差值作为雾与地物识别分类标志,建立基于SBDART辐射传输模型和BP神经网络的夜间雾遥感检测和能见度反演模型。对2007年11月24日我国华北地区的一次陆地辐射雾MODIS卫星数据进行雾检测,同时反演雾区能见度。根据陕西省气象局提供的地面气象观测数据对模型雾检测结果和能见度反演结果进行验证,该次实验夜间雾检测的准确率为79.2%,地面观测能见度和反演能见度一元线性回归分析方程斜率为1.006,相关系数为0.8498。实验结果表明,模型具有较高的雾识别率和雾能见度反演结果,可为夜间雾识别和生消发展规律探讨提供一定的帮助。
Fog is a disastrous weather phenomenon,which seriously influences people's daily life and economic activities.A nighttime fog detection and visibility retrieval model was established based on SBDART(santa barbara DISORT atmospheric radiative transfer)and BP(back propagation)Neural Networks using brightness temperature information and difference information between MIR and TIR.The model was tested in the North China Plain on November 24,2007,with 1km resolution MODIS/TERRA images,and the fog detection accuracies and visibility retrieval result were evaluated using the observation results from Shanxi Meteorological Bureau.Results show that the accuracy of fog detection is 79.2%,the slope is 1.006and the correlation coefficient is 0.8498of unary linear regression between the visibility observation values and the inversion visibility values.The model can provide strong support for the fog detection and visibility retrieval,which can contribute to further understanding of the development and extinction discipline of nighttime radiation fog.
Fog is a disastrous weather phenomenon,which seriously influences people's daily life and economic activities.A nighttime fog detection and visibility retrieval model was established based on SBDART(santa barbara DISORT atmospheric radiative transfer)and BP(back propagation)Neural Networks using brightness temperature information and difference information between MIR and TIR.The model was tested in the North China Plain on November 24,2007,with 1km resolution MODIS/TERRA images,and the fog detection accuracies and visibility retrieval result were evaluated using the observation results from Shanxi Meteorological Bureau.Results show that the accuracy of fog detection is 79.2%,the slope is 1.006and the correlation coefficient is 0.8498of unary linear regression between the visibility observation values and the inversion visibility values.The model can provide strong support for the fog detection and visibility retrieval,which can contribute to further understanding of the development and extinction discipline of nighttime radiation fog.
引文
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[3]BENDIX J.A case study on the determination of fog optical depth and liquid water path using AVHRR data and relations to fog liquid water content and horizontal visibility[J].Remote Sensing,1995,16(3):515-530.
[4]孙涵,孙照渤,李亚春.雾的气象卫星遥感光谱特征[J].南京气象学院学报,2004,27(3):289-301.SUN Han,SUN Zhaobo,LI Yachun.Meteorological satellite remote-sensing spectral characteristics of fog[J].Journal of Nanjing Institute of Meteorology,2004,27(3):289-301.(in Chinese).
[5]吴晓京,陈云浩,李三妹.应用MODIS数据对新疆北部大雾地面能见度和微物理参数的反演[J].遥感学报,2005,9(6):688-696.WU Xiaojing,CHENG Yunhao,LI Sanmei.Utilizing MODIS data to retrieve the visibility and microphysical properties of fog happens in the Northwest China[J].Journal of Remote Sensing,2005,9(6):688-696.(in Chinese).
[6]蒋璐璐,魏鸣.FY-3A卫星资料在雾监测中的应用研究[J].遥感技术与应用,2011,26(4):489-495.JIANG Lulu,WEI Ming.FY-3Aapplication of fog monitoring with FY-3Adata[J].Remote Sensing Technology and Application,2011,26(4):489-495.(in Chinese).
[7]刘香娥,王广河.FY-2C静止气象卫星红外通道辐射特性研究[J].气象,2010,36(2):39-47.LIU Xiang'e,WANG Guanghe.Analyses of the radiation characteristics from FY2Cmid-infrared channel[J].Meteorological Monthly,2010,36(2):39-47.(in Chinese).
[8]张顺谦,杨秀蓉.基于神经网络和分形纹理的夜间浓雾遥感监测技术[J].应用气象学报,2005,16(6):804-810.ZHANG Shunqian,YANG Xiurong.Remote sensing monitoring technology of thick fog at night based on neural networks and rectal grain[J].Journal of Applied Meteorological Science,2005,16(6):804-810.(in Chinese).
[9]LAN Yang,MING Wei.Using MODIS data to retrieve microphysical properties of fog in Yangtze River delta[C].Shanghai:International Workshop on Geosciences and Remote Sensing,2008.
[10]BENDIX J.Determination of fog horizontal visibility by means of NOAA-AVHRR[J].1995,3,1847-1849.
[11]中国气象局.地面气象观测规范[S].北京:气象出版社.2003.
[12]马慧云,李德仁,刘良明,等.基于AVHRR、MODIS和MVRIS数据的辐射雾变化检测与分析[J].武汉大学学报,信息科学版,2007,32(4):297-300.MA Huiyun,LI Deren,LIU Liangming,et al.Fog dynamic change detection based on AVHRR,MODIS and MVIRS data[J].Geomantic and Information Science of Wuhan University,2007,32(4):297-300.(in Chinese).
[13]王丽萍,陈少勇,董安祥.中国雾区的分布及其季节变化[J].地理学报,2005,60(4):689-697.WANG Liping,CHEN Shaoyong,DONG Anxiang.The distribution and seasonal variations of fog in China[J].Acta Geographica Sinica,2005,60(4):689-697.(in Chinese).
[14]李子华.中国近40年来雾的研究[J].气象学报,2001,59(5):616-623.LI Zihua.Studies of fog in China over the past 40years[J].Acta Meteorologica Sinica,2001,59(5):616-623.(in Chinese).
[15]CERMAK J,BENDIX J.Dynamical nighttime fog/low stratus detection based on meteosat SEVIRI data:a feasibility study[J].Pure Appl Geophys,2007,164,1179-1192.